TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Gulde, Thomas
A1  - Ludl, Dennis
A1  - Curio, Cristóbal
T1  - RoPose: CNN-based 2D pose estimation of industrial robots
T2  - 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) : Munich, Germany, August 20-24, 2018
N2  - As production workspaces become more mobile and dynamic it becomes increasingly important to reliably monitor the overall state of the environment. Therein manipulators or other robotic systems likely have to be able to act autonomously together with humans and other systems within a joint workspace. Such interactions require that all components in non-stationary environments are able to perceive the state relative to each other. As vision-sensors provide a rich source of information to accomplish this, we present RoPose, a convolutional neural network (CNN) based approach, to estimate the two dimensional joint configuration of a simulated industrial manipulator from a camera image. This pose information can further be used by a novel targetless calibration setup to estimate the pose of the camera relative to the manipulator’s space. We present a pipeline to automatically generate synthetic training data and conclude with a discussion of the potential usage of the same pipeline to acquire real image datasets of physically existent robots.
Y1  - 2018
U6  - https://doi.org/10.1109/COASE.2018.8560564
DO  - https://doi.org/10.1109/COASE.2018.8560564
SP  - 463
EP  - 470
S1  - 8
PB  - IEEE
CY  - Piscataway, NJ
ER  -